Improving salesforce performance: A meta-analytic investigation of the effectiveness and utility of personnel selection procedures and training interventions
Bibliographic record
Abstract
Research on the effectiveness in improving salesforce performance through personnel selection procedures and training interventions was examined by meta-analytic techniques applied with 157 predictor-criterion effect sizes involving selection procedures and 12 effect sizes involving training interventions. Significant effect sizes, on average, were obtained for (a) composite-domain assessment against both subjective (ratings) and objective (sales performance) criteria, (b) single-domain assessment against both criterion types, and (c) training interventions with respect to both criterion types combined. Significant variability was found among individual effect sizes within all categories of aggregation. Of the six specific categories of single-domain assessment considered, five yielded significant validity for each of the two criterion types. Follow-up utility analyses revealed improvements in sales productivity of from 14.8% to 34.1% for selection procedures and of 23.1% for training. Associated dollar-based utility estimates indicated particularly substantial dollar gains for organizations employing composite-domain selection with rigorous selection ratios, and lesser, but still substantial, gains from single-domain selection with rigorous selection ratios, and from training interventions. © 2001 John Wiley & Sons, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".